Raison du choix de MEP:
“Je serais plutôt pour tester un de ceux qui sort dans le papier d’Arianne, au choix : MEP, MBzP ou MnBP. Prends en un au hasard.” e-mail Claire 23/11/20
Since last time:
To do:
On 10/20/2020 we discussed with CP of babylab’s analysis plan (cf meeting notes in project_log.html and model_choice.html. We still had questions regarding multiple exposures in the same model (T2 and T3) and regarding interactions between timing of exposure (T2 and T3) and timing of eyetracker experiment (M5, M12, M24). To finalise our analysis plan we decided to perform the following analysis:
=> MEP looks log noramal(-ish), will include as log in the model
Arithmetic mean of “mean fixation duration” at face recognition and scene exploration tasks:
Selected covariates and coding see dag_var_coding.html:
At 24m
##
## Call:
## lm(formula = fix_dur ~ mo_edu + ch_sex + mo_par + mo_tob + mo_age +
## mo_had_cat + exp_t_cat, data = model_data_24)
##
## Residuals:
## Min 1Q Median 3Q Max
## -94.343 -29.934 -8.229 30.164 159.184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 260.3684 35.2128 7.394 1.55e-11 ***
## mo_edu<=bac+2 -7.9042 12.2693 -0.644 0.52056
## ch_sex2 -4.3444 8.4277 -0.515 0.60708
## mo_par1 -1.9774 9.0759 -0.218 0.82787
## mo_par2 7.5433 17.3116 0.436 0.66375
## mo_tob 34.3024 13.4164 2.557 0.01171 *
## mo_age 3.1132 1.1129 2.797 0.00594 **
## mo_had_cat(8,11] -12.4455 10.2704 -1.212 0.22779
## mo_had_cat(11,25] 7.4022 10.2970 0.719 0.47351
## exp_t_cat(10,11] -22.5877 14.6573 -1.541 0.12573
## exp_t_cat(11,14] 5.5768 16.5712 0.337 0.73701
## exp_t_cat(14,16] -0.1187 12.6825 -0.009 0.99255
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 48.83 on 130 degrees of freedom
## (9 observations deleted due to missingness)
## Multiple R-squared: 0.1404, Adjusted R-squared: 0.06763
## F-statistic: 1.93 on 11 and 130 DF, p-value: 0.04103
For values and p vals check annex.
Linear regression for each age group (24m, 12m and 5m):
Mixed effect model for all age groups combined (repeated measures):
Mixed effect model with interaction term to combine age groups (repeated measures) and take into account possible differences in effect between time of exposure and age of measurement:
=> decide on whether we want to include T2 and T3 in the same model
Note that interaction terms are not projected for the age group, they are just the interaction term.
to discuss with claire
=> I would do separate mixed models for T2 and T3 (most power/sensitive, more interesting interpretation wise) with the following sensitivity analyses
## # A tibble: 16 x 6
## term estimate conf.low conf.high model age
## <chr> <dbl> <dbl> <dbl> <chr> <fct>
## 1 log(MEP_T1) 0.661 -7.49 8.81 T2 24m
## 2 log(MEP_T3) 5.20 -3.01 13.4 T3 24m
## 3 log(MEP_T3) 7.69 -2.74 18.1 T2 + T3 24m
## 4 log(MEP_T1) -3.93 -14.2 6.29 T2 + T3 24m
## 5 log(MEP_T1) 8.38 -5.49 22.3 T2 12m
## 6 log(MEP_T3) 7.37 -7.42 22.2 T3 12m
## 7 log(MEP_T3) 3.17 -15.4 21.7 T2 + T3 12m
## 8 log(MEP_T1) 6.59 -10.9 24.1 T2 + T3 12m
## 9 log(MEP_T1) 6.96 -12.0 25.9 T2 5m
## 10 log(MEP_T3) 3.15 -20.7 27.0 T3 5m
## 11 log(MEP_T3) -5.59 -39.1 28.0 T2 + T3 5m
## 12 log(MEP_T1) 10.1 -16.8 36.9 T2 + T3 5m
## 13 log(MEP_T1) 2.96 -4.06 9.98 T2 mixed
## 14 log(MEP_T3) 5.47 -1.82 12.8 T3 mixed
## 15 log(MEP_T3) 5.84 -3.49 15.2 T2 + T3 mixed
## 16 log(MEP_T1) -0.475 -9.38 8.44 T2 + T3 mixed